Support Anima (#1317)

* support Anima Co-authored-by: mi804 <1576993271@qq.com>
2026-03-18 22:08:13 +00:00 · 2026-03-02 18:49:02 +08:00
parent 880231b4be
commit 6d671db5d2
18 changed files with 2252 additions and 1 deletions
--- a/README.md
+++ b/README.md
@@ -32,6 +32,9 @@ We believe that a well-developed open-source code framework can lower the thresh
 > DiffSynth-Studio has undergone major version updates, and some old features are no longer maintained. If you need to use old features, please switch to the [last historical version](https://github.com/modelscope/DiffSynth-Studio/tree/afd101f3452c9ecae0c87b79adfa2e22d65ffdc3) before the major version update.

 > Currently, the development personnel of this project are limited, with most of the work handled by [Artiprocher](https://github.com/Artiprocher). Therefore, the progress of new feature development will be relatively slow, and the speed of responding to and resolving issues is limited. We apologize for this and ask developers to understand.
+
+- **March 2, 2026** Added support for [Anima](https://modelscope.cn/models/circlestone-labs/Anima). For details, please refer to the [documentation](docs/en/Model_Details/Anima.md). This is an interesting anime-style image generation model. We look forward to its future updates.
+
 - **February 26, 2026** Added full and lora training support for the LTX-2 audio-video generation model. See the [documentation](/docs/en/Model_Details/LTX-2.md) for details.

 - **February 10, 2026** Added inference support for the LTX-2 audio-video generation model. See the [documentation](/docs/en/Model_Details/LTX-2.md) for details. Support for model training will be implemented in the future.
@@ -343,6 +346,60 @@ Example code for FLUX.2 is available at: [/examples/flux2/](/examples/flux2/)

 </details>

+#### Anima: [/docs/en/Model_Details/Anima.md](/docs/en/Model_Details/Anima.md)
+
+<details>
+
+<summary>Quick Start</summary>
+
+Run the following code to quickly load the [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) model and perform inference. VRAM management is enabled, and the framework will automatically control the loading of model parameters based on available VRAM. The model can run with a minimum of 8GB VRAM.
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>Examples</summary>
+
+Example code for Anima is located at: [/examples/anima/](/examples/anima/)
+
+| Model ID | Inference | Low VRAM Inference | Full Training | Validation after Full Training | LoRA Training | Validation after LoRA Training |
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+</details>
+
 #### Qwen-Image: [/docs/en/Model_Details/Qwen-Image.md](/docs/en/Model_Details/Qwen-Image.md)

 <details>
--- a/README_zh.md
+++ b/README_zh.md
@@ -32,6 +32,9 @@ DiffSynth 目前包括两个开源项目：
 > DiffSynth-Studio 经历了大版本更新，部分旧功能已停止维护，如需使用旧版功能，请切换到大版本更新前的[最后一个历史版本](https://github.com/modelscope/DiffSynth-Studio/tree/afd101f3452c9ecae0c87b79adfa2e22d65ffdc3)。

 > 目前本项目的开发人员有限，大部分工作由 [Artiprocher](https://github.com/Artiprocher) 负责，因此新功能的开发进展会比较缓慢，issue 的回复和解决速度有限，我们对此感到非常抱歉，请各位开发者理解。
+
+- **2026年3月2日** 新增对[Anima](https://modelscope.cn/models/circlestone-labs/Anima)的支持，详见[文档](docs/zh/Model_Details/Anima.md)。这是一个有趣的动漫风格图像生成模型，我们期待其后续的模型更新。
+
 - **2026年2月26日** 新增对[LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2)音视频生成模型全量微调与LoRA训练支持，详见[文档](docs/zh/Model_Details/LTX-2.md)。

 - **2026年2月10日** 新增对[LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2)音视频生成模型的推理支持，详见[文档](docs/zh/Model_Details/LTX-2.md)，后续将推进模型训练的支持。
@@ -343,6 +346,60 @@ FLUX.2 的示例代码位于：[/examples/flux2/](/examples/flux2/)

 </details>

+#### Anima: [/docs/zh/Model_Details/Anima.md](/docs/zh/Model_Details/Anima.md)
+
+<details>
+
+<summary>快速开始</summary>
+
+运行以下代码可以快速加载 [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) 模型并进行推理。显存管理已启动，框架会自动根据剩余显存控制模型参数的加载，最低 8G 显存即可运行。
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>示例代码</summary>
+
+Anima 的示例代码位于：[/examples/anima/](/examples/anima/)
+
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+</details>
+
 #### Qwen-Image: [/docs/zh/Model_Details/Qwen-Image.md](/docs/zh/Model_Details/Qwen-Image.md)

 <details>
--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -719,4 +719,20 @@ ltx2_series = [
        "model_class": "diffsynth.models.ltx2_upsampler.LTX2LatentUpsampler",
    },
 ]
-MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series
+anima_series = [
+    {
+        # Example: ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors")
+        "model_hash": "a9995952c2d8e63cf82e115005eb61b9",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "0.6B"},
+    },
+    {
+        # Example: ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors")
+        "model_hash": "417673936471e79e31ed4d186d7a3f4a",
+        "model_name": "anima_dit",
+        "model_class": "diffsynth.models.anima_dit.AnimaDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.anima_dit.AnimaDiTStateDictConverter",
+    }
+]
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series + anima_series
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -243,4 +243,10 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
        "transformers.models.gemma3.modeling_gemma3.Gemma3RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
        "transformers.models.gemma3.modeling_gemma3.Gemma3TextScaledWordEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
    },
+    "diffsynth.models.anima_dit.AnimaDiT": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
 }
--- a/diffsynth/models/anima_dit.py
+++ b/diffsynth/models/anima_dit.py
--- a/diffsynth/pipelines/anima_image.py
+++ b/diffsynth/pipelines/anima_image.py
@@ -0,0 +1,261 @@
+import torch, math
+from PIL import Image
+from typing import Union
+from tqdm import tqdm
+from einops import rearrange
+import numpy as np
+from math import prod
+from transformers import AutoTokenizer
+
+from ..core.device.npu_compatible_device import get_device_type
+from ..diffusion import FlowMatchScheduler
+from ..core import ModelConfig, gradient_checkpoint_forward
+from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
+from ..utils.lora.merge import merge_lora
+
+from ..models.anima_dit import AnimaDiT
+from ..models.z_image_text_encoder import ZImageTextEncoder
+from ..models.wan_video_vae import WanVideoVAE
+
+
+class AnimaImagePipeline(BasePipeline):
+
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device, torch_dtype=torch_dtype,
+            height_division_factor=16, width_division_factor=16,
+        )
+        self.scheduler = FlowMatchScheduler("Z-Image")
+        self.text_encoder: ZImageTextEncoder = None
+        self.dit: AnimaDiT = None
+        self.vae: WanVideoVAE = None
+        self.tokenizer: AutoTokenizer = None
+        self.tokenizer_t5xxl: AutoTokenizer = None
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            AnimaUnit_ShapeChecker(),
+            AnimaUnit_NoiseInitializer(),
+            AnimaUnit_InputImageEmbedder(),
+            AnimaUnit_PromptEmbedder(),
+        ]
+        self.model_fn = model_fn_anima
+    
+    
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = get_device_type(),
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+        tokenizer_t5xxl_config: ModelConfig = ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+        vram_limit: float = None,
+    ):
+        # Initialize pipeline
+        pipe = AnimaImagePipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+        
+        # Fetch models
+        pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
+        pipe.dit = model_pool.fetch_model("anima_dit")
+        pipe.vae = model_pool.fetch_model("wan_video_vae")
+        if tokenizer_config is not None:
+            tokenizer_config.download_if_necessary()
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
+        if tokenizer_t5xxl_config is not None:
+            tokenizer_t5xxl_config.download_if_necessary()
+            pipe.tokenizer_t5xxl = AutoTokenizer.from_pretrained(tokenizer_t5xxl_config.path)
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+    
+    
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: str = "",
+        cfg_scale: float = 4.0,
+        # Image
+        input_image: Image.Image = None,
+        denoising_strength: float = 1.0,
+        # Shape
+        height: int = 1024,
+        width: int = 1024,
+        # Randomness
+        seed: int = None,
+        rand_device: str = "cpu",
+        # Steps
+        num_inference_steps: int = 30,
+        sigma_shift: float = None,
+        # Progress bar
+        progress_bar_cmd = tqdm,
+    ):
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
+        
+        # Parameters
+        inputs_posi = {
+            "prompt": prompt,
+        }
+        inputs_nega = {
+            "negative_prompt": negative_prompt,
+        }
+        inputs_shared = {
+            "cfg_scale": cfg_scale,
+            "input_image": input_image, "denoising_strength": denoising_strength,
+            "height": height, "width": width,
+            "seed": seed, "rand_device": rand_device,
+            "num_inference_steps": num_inference_steps,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
+        
+        # Decode
+        self.load_models_to_device(['vae'])
+        image = self.vae.decode(inputs_shared["latents"].unsqueeze(2), device=self.device).squeeze(2)
+        image = self.vae_output_to_image(image)
+        self.load_models_to_device([])
+
+        return image
+
+
+class AnimaUnit_ShapeChecker(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width"),
+            output_params=("height", "width"),
+        )
+
+    def process(self, pipe: AnimaImagePipeline, height, width):
+        height, width = pipe.check_resize_height_width(height, width)
+        return {"height": height, "width": width}
+
+
+
+class AnimaUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "seed", "rand_device"),
+            output_params=("noise",),
+        )
+
+    def process(self, pipe: AnimaImagePipeline, height, width, seed, rand_device):
+        noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+        return {"noise": noise}
+
+
+
+class AnimaUnit_InputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: AnimaImagePipeline, input_image, noise):
+        if input_image is None:
+            return {"latents": noise, "input_latents": None}
+        pipe.load_models_to_device(['vae'])
+        if isinstance(input_image, list):
+            input_latents = []
+            for image in input_image:
+                image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype)
+                input_latents.append(pipe.vae.encode(image))
+            input_latents = torch.concat(input_latents, dim=0)
+        else:
+            image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
+            input_latents = pipe.vae.encode(image.unsqueeze(2), device=pipe.device).squeeze(2)
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents, "input_latents": input_latents}
+
+
+class AnimaUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_emb",),
+            onload_model_names=("text_encoder",)
+        )
+
+    def encode_prompt(
+        self,
+        pipe: AnimaImagePipeline,
+        prompt,
+        device = None,
+        max_sequence_length: int = 512,
+    ):
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        text_inputs = pipe.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        prompt_masks = text_inputs.attention_mask.to(device).bool()
+
+        prompt_embeds = pipe.text_encoder(
+            input_ids=text_input_ids,
+            attention_mask=prompt_masks,
+            output_hidden_states=True,
+        ).hidden_states[-1]
+        
+        t5xxl_text_inputs = pipe.tokenizer_t5xxl(
+            prompt,
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        t5xxl_ids = t5xxl_text_inputs.input_ids.to(device)
+
+        return prompt_embeds.to(pipe.torch_dtype), t5xxl_ids
+
+    def process(self, pipe: AnimaImagePipeline, prompt):
+        pipe.load_models_to_device(self.onload_model_names)
+        prompt_embeds, t5xxl_ids = self.encode_prompt(pipe, prompt, pipe.device)
+        return {"prompt_emb": prompt_embeds, "t5xxl_ids": t5xxl_ids}
+
+
+def model_fn_anima(
+    dit: AnimaDiT = None,
+    latents=None,
+    timestep=None,
+    prompt_emb=None,
+    t5xxl_ids=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs
+):
+    latents = latents.unsqueeze(2)
+    timestep = timestep / 1000
+    model_output = dit(
+        x=latents,
+        timesteps=timestep,
+        context=prompt_emb,
+        t5xxl_ids=t5xxl_ids,
+    )
+    model_output = model_output.squeeze(2)
+    return model_output
--- a/diffsynth/utils/state_dict_converters/anima_dit.py
+++ b/diffsynth/utils/state_dict_converters/anima_dit.py
@@ -0,0 +1,6 @@
+def AnimaDiTStateDictConverter(state_dict):
+    new_state_dict = {}
+    for key in state_dict:
+        value = state_dict[key]
+        new_state_dict[key.replace("net.", "")] = value
+    return new_state_dict
--- a/docs/en/Model_Details/Anima.md
+++ b/docs/en/Model_Details/Anima.md
@@ -0,0 +1,139 @@
+# Anima
+
+Anima is an image generation model trained and open-sourced by CircleStone Labs and Comfy Org.
+
+## Installation
+
+Before using this project for model inference and training, please install DiffSynth-Studio first.
+
+```shell
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+For more installation information, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
+
+## Quick Start
+
+The following code demonstrates how to quickly load the [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) model for inference. VRAM management is enabled by default, allowing the framework to automatically control model parameter loading based on available VRAM. Minimum 8GB VRAM required.
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+## Model Overview
+
+|Model ID|Inference|Low VRAM Inference|Full Training|Post-Full Training Validation|LoRA Training|Post-LoRA Training Validation|
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+Special training scripts:
+
+* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
+* Two-Stage Split Training: [doc](../Training/Split_Training.md)
+* End-to-End Direct Distillation: [doc](../Training/Direct_Distill.md)
+
+## Model Inference
+
+Models are loaded through `AnimaImagePipeline.from_pretrained`, see [Model Inference](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
+
+Input parameters for `AnimaImagePipeline` inference include:
+
+* `prompt`: Text description of the desired image content.
+* `negative_prompt`: Content to exclude from the generated image (default: `""`).
+* `cfg_scale`: Classifier-free guidance parameter (default: 4.0).
+* `input_image`: Input image for image-to-image generation (default: `None`).
+* `denoising_strength`: Controls similarity to input image (default: 1.0).
+* `height`: Image height (must be multiple of 16, default: 1024).
+* `width`: Image width (must be multiple of 16, default: 1024).
+* `seed`: Random seed (default: `None`).
+* `rand_device`: Device for random noise generation (default: `"cpu"`).
+* `num_inference_steps`: Inference steps (default: 30).
+* `sigma_shift`: Scheduler sigma offset (default: `None`).
+* `progress_bar_cmd`: Progress bar implementation (default: `tqdm.tqdm`).
+
+For VRAM constraints, enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). Recommended low-VRAM configurations are provided in the "Model Overview" table above.
+
+## Model Training
+
+Anima models are trained through [`examples/anima/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/train.py) with parameters including:
+
+* General Training Parameters
+    * Dataset Configuration
+        * `--dataset_base_path`: Dataset root directory.
+        * `--dataset_metadata_path`: Metadata file path.
+        * `--dataset_repeat`: Dataset repetition per epoch.
+        * `--dataset_num_workers`: Dataloader worker count.
+        * `--data_file_keys`: Metadata fields to load (comma-separated).
+    * Model Loading
+        * `--model_paths`: Model paths (JSON format).
+        * `--model_id_with_origin_paths`: Model IDs with origin paths (e.g., `"anima-team/anima-1B:text_encoder/*.safetensors"`).
+        * `--extra_inputs`: Additional pipeline inputs (e.g., `controlnet_inputs` for ControlNet).
+        * `--fp8_models`: FP8-formatted models (same format as `--model_paths`).
+    * Training Configuration
+        * `--learning_rate`: Learning rate.
+        * `--num_epochs`: Training epochs.
+        * `--trainable_models`: Trainable components (e.g., `dit`, `vae`, `text_encoder`).
+        * `--find_unused_parameters`: Handle unused parameters in DDP training.
+        * `--weight_decay`: Weight decay value.
+        * `--task`: Training task (default: `sft`).
+    * Output Configuration
+        * `--output_path`: Model output directory.
+        * `--remove_prefix_in_ckpt`: Remove state dict prefixes.
+        * `--save_steps`: Model saving interval.
+    * LoRA Configuration
+        * `--lora_base_model`: Target model for LoRA.
+        * `--lora_target_modules`: Target modules for LoRA.
+        * `--lora_rank`: LoRA rank.
+        * `--lora_checkpoint`: LoRA checkpoint path.
+        * `--preset_lora_path`: Preloaded LoRA checkpoint path.
+        * `--preset_lora_model`: Model to merge LoRA with (e.g., `dit`).
+    * Gradient Configuration
+        * `--use_gradient_checkpointing`: Enable gradient checkpointing.
+        * `--use_gradient_checkpointing_offload`: Offload checkpointing to CPU.
+        * `--gradient_accumulation_steps`: Gradient accumulation steps.
+    * Image Resolution
+        * `--height`: Image height (empty for dynamic resolution).
+        * `--width`: Image width (empty for dynamic resolution).
+        * `--max_pixels`: Maximum pixel area for dynamic resolution.
+* Anima-Specific Parameters
+    * `--tokenizer_path`: Tokenizer path for text-to-image models.
+    * `--tokenizer_t5xxl_path`: T5-XXL tokenizer path.
+
+We provide a sample image dataset for testing:
+
+```shell
+modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+```
+
+For training script details, refer to [Model Training](../Pipeline_Usage/Model_Training.md). For advanced training techniques, see [Training Framework Documentation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/).
--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@@ -27,6 +27,7 @@ Welcome to DiffSynth-Studio's Documentation
   Model_Details/Qwen-Image
   Model_Details/FLUX2
   Model_Details/Z-Image
+   Model_Details/Anima
   Model_Details/LTX-2

 .. toctree::
--- a/docs/zh/Model_Details/Anima.md
+++ b/docs/zh/Model_Details/Anima.md
@@ -0,0 +1,139 @@
+# Anima
+
+Anima 是由 CircleStone Labs 与 Comfy Org 训练并开源的图像生成模型。
+
+## 安装
+
+在使用本项目进行模型推理和训练前，请先安装 DiffSynth-Studio。
+
+```shell
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
+
+## 快速开始
+
+运行以下代码可以快速加载 [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) 模型并进行推理。显存管理已启动，框架会自动根据剩余显存控制模型参数的加载，最低 8G 显存即可运行。
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+## 模型总览
+
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+特殊训练脚本：
+
+* 差分 LoRA 训练：[doc](../Training/Differential_LoRA.md)
+* FP8 精度训练：[doc](../Training/FP8_Precision.md)
+* 两阶段拆分训练：[doc](../Training/Split_Training.md)
+* 端到端直接蒸馏：[doc](../Training/Direct_Distill.md)
+
+## 模型推理
+
+模型通过 `AnimaImagePipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
+
+`AnimaImagePipeline` 推理的输入参数包括：
+
+* `prompt`: 提示词，描述画面中出现的内容。
+* `negative_prompt`: 负向提示词，描述画面中不应该出现的内容，默认值为 `""`。
+* `cfg_scale`: Classifier-free guidance 的参数，默认值为 4.0。
+* `input_image`: 输入图像，用于图像到图像的生成。默认为 `None`。
+* `denoising_strength`: 去噪强度，控制生成图像与输入图像的相似度，默认值为 1.0。
+* `height`: 图像高度，需保证高度为 16 的倍数，默认值为 1024。
+* `width`: 图像宽度，需保证宽度为 16 的倍数，默认值为 1024。
+* `seed`: 随机种子。默认为 `None`，即完全随机。
+* `rand_device`: 生成随机高斯噪声矩阵的计算设备，默认为 `"cpu"`。当设置为 `cuda` 时，在不同 GPU 上会导致不同的生成结果。
+* `num_inference_steps`: 推理次数，默认值为 30。
+* `sigma_shift`: 调度器的 sigma 偏移量，默认为 `None`。
+* `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。
+
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
+
+## 模型训练
+
+Anima 系列模型统一通过 [`examples/anima/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/train.py) 进行训练，脚本的参数包括：
+
+* 通用训练参数
+    * 数据集基础配置
+        * `--dataset_base_path`: 数据集的根目录。
+        * `--dataset_metadata_path`: 数据集的元数据文件路径。
+        * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
+        * `--dataset_num_workers`: 每个 Dataloder 的进程数量。
+        * `--data_file_keys`: 元数据中需要加载的字段名称，通常是图像或视频文件的路径，以 `,` 分隔。
+    * 模型加载配置
+        * `--model_paths`: 要加载的模型路径。JSON 格式。
+        * `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 `"anima-team/anima-1B:text_encoder/*.safetensors"`。用逗号分隔。
+        * `--extra_inputs`: 模型 Pipeline 所需的额外输入参数，例如训练 ControlNet 模型时需要额外参数 `controlnet_inputs`，以 `,` 分隔。
+        * `--fp8_models`：以 FP8 格式加载的模型，格式与 `--model_paths` 或 `--model_id_with_origin_paths` 一致，目前仅支持参数不被梯度更新的模型（不需要梯度回传，或梯度仅更新其 LoRA）。
+    * 训练基础配置
+        * `--learning_rate`: 学习率。
+        * `--num_epochs`: 轮数（Epoch）。
+        * `--trainable_models`: 可训练的模型，例如 `dit`、`vae`、`text_encoder`。
+        * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数，少数模型包含不参与梯度计算的冗余参数，需开启这一设置避免在多 GPU 训练中报错。
+        * `--weight_decay`：权重衰减大小，详见 [torch.optim.AdamW](https://docs.pytorch.org/docs/stable/generated/torch.optim.AdamW.html)。
+        * `--task`: 训练任务，默认为 `sft`，部分模型支持更多训练模式，请参考每个特定模型的文档。
+    * 输出配置
+        * `--output_path`: 模型保存路径。
+        * `--remove_prefix_in_ckpt`: 在模型文件的 state dict 中移除前缀。
+        * `--save_steps`: 保存模型的训练步数间隔，若此参数留空，则每个 epoch 保存一次。
+    * LoRA 配置
+        * `--lora_base_model`: LoRA 添加到哪个模型上。
+        * `--lora_target_modules`: LoRA 添加到哪些层上。
+        * `--lora_rank`: LoRA 的秩（Rank）。
+        * `--lora_checkpoint`: LoRA 检查点的路径。如果提供此路径，LoRA 将从此检查点加载。
+        * `--preset_lora_path`: 预置 LoRA 检查点路径，如果提供此路径，这一 LoRA 将会以融入基础模型的形式加载。此参数用于 LoRA 差分训练。
+        * `--preset_lora_model`: 预置 LoRA 融入的模型，例如 `dit`。
+    * 梯度配置
+        * `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。
+        * `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。
+        * `--gradient_accumulation_steps`: 梯度累积步数。
+    * 图像宽高配置（适用于图像生成模型和视频生成模型）
+        * `--height`: 图像或视频的高度。将 `height` 和 `width` 留空以启用动态分辨率。
+        * `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。
+        * `--max_pixels`: 图像或视频帧的最大像素面积，当启用动态分辨率时，分辨率大于这个数值的图片都会被缩小，分辨率小于这个数值的图片保持不变。
+* Anima 专有参数
+    * `--tokenizer_path`: tokenizer 的路径，适用于文生图模型，留空则自动从远程下载。
+    * `--tokenizer_t5xxl_path`: T5-XXL tokenizer 的路径，适用于文生图模型，留空则自动从远程下载。
+
+我们构建了一个样例图像数据集，以方便您进行测试，通过以下命令可以下载这个数据集：
+
+```shell
+modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+```
+
+我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
--- a/docs/zh/index.rst
+++ b/docs/zh/index.rst
@@ -27,6 +27,7 @@
   Model_Details/Qwen-Image
   Model_Details/FLUX2
   Model_Details/Z-Image
+   Model_Details/Anima
   Model_Details/LTX-2

 .. toctree::
--- a/examples/anima/model_inference/anima-preview.py
+++ b/examples/anima/model_inference/anima-preview.py
@@ -0,0 +1,19 @@
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors"),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors"),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/")
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
--- a/examples/anima/model_inference_low_vram/anima-preview.py
+++ b/examples/anima/model_inference_low_vram/anima-preview.py
@@ -0,0 +1,30 @@
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
--- a/examples/anima/model_training/full/anima-preview.sh
+++ b/examples/anima/model_training/full/anima-preview.sh
@@ -0,0 +1,14 @@
+accelerate launch examples/anima/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "circlestone-labs/Anima:split_files/diffusion_models/anima-preview.safetensors,circlestone-labs/Anima:split_files/text_encoders/qwen_3_06b_base.safetensors,circlestone-labs/Anima:split_files/vae/qwen_image_vae.safetensors" \
+  --tokenizer_path "Qwen/Qwen3-0.6B:./" \
+  --tokenizer_t5xxl_path "stabilityai/stable-diffusion-3.5-large:tokenizer_3/" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/anima-preview_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing
--- a/examples/anima/model_training/lora/anima-preview.sh
+++ b/examples/anima/model_training/lora/anima-preview.sh
@@ -0,0 +1,16 @@
+accelerate launch examples/anima/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "circlestone-labs/Anima:split_files/diffusion_models/anima-preview.safetensors,circlestone-labs/Anima:split_files/text_encoders/qwen_3_06b_base.safetensors,circlestone-labs/Anima:split_files/vae/qwen_image_vae.safetensors" \
+  --tokenizer_path "Qwen/Qwen3-0.6B:./" \
+  --tokenizer_t5xxl_path "stabilityai/stable-diffusion-3.5-large:tokenizer_3/" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/anima-preview_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing
--- a/examples/anima/model_training/train.py
+++ b/examples/anima/model_training/train.py
@@ -0,0 +1,145 @@
+import torch, os, argparse, accelerate
+from diffsynth.core import UnifiedDataset
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+from diffsynth.diffusion import *
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+
+class AnimaTrainingModule(DiffusionTrainingModule):
+    def __init__(
+        self,
+        model_paths=None, model_id_with_origin_paths=None,
+        tokenizer_path=None, tokenizer_t5xxl_path=None,
+        trainable_models=None,
+        lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
+        preset_lora_path=None, preset_lora_model=None,
+        use_gradient_checkpointing=True,
+        use_gradient_checkpointing_offload=False,
+        extra_inputs=None,
+        fp8_models=None,
+        offload_models=None,
+        device="cpu",
+        task="sft",
+    ):
+        super().__init__()
+        # Load models
+        model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
+        tokenizer_config = self.parse_path_or_model_id(tokenizer_path, ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"))
+        tokenizer_t5xxl_config = self.parse_path_or_model_id(tokenizer_t5xxl_path, ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"))
+        self.pipe = AnimaImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, tokenizer_t5xxl_config=tokenizer_t5xxl_config)
+        self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
+
+        # Training mode
+        self.switch_pipe_to_training_mode(
+            self.pipe, trainable_models,
+            lora_base_model, lora_target_modules, lora_rank, lora_checkpoint,
+            preset_lora_path, preset_lora_model,
+            task=task,
+        )
+        
+        # Other configs
+        self.use_gradient_checkpointing = use_gradient_checkpointing
+        self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
+        self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
+        self.fp8_models = fp8_models
+        self.task = task
+        self.task_to_loss = {
+            "sft:data_process": lambda pipe, *args: args,
+            "direct_distill:data_process": lambda pipe, *args: args,
+            "sft": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
+            "sft:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: FlowMatchSFTLoss(pipe, **inputs_shared, **inputs_posi),
+            "direct_distill": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi),
+            "direct_distill:train": lambda pipe, inputs_shared, inputs_posi, inputs_nega: DirectDistillLoss(pipe, **inputs_shared, **inputs_posi),
+        }
+        
+    def get_pipeline_inputs(self, data):
+        inputs_posi = {"prompt": data["prompt"]}
+        inputs_nega = {"negative_prompt": ""}
+        inputs_shared = {
+            # Assume you are using this pipeline for inference,
+            # please fill in the input parameters.
+            "input_image": data["image"],
+            "height": data["image"].size[1],
+            "width": data["image"].size[0],
+            # Please do not modify the following parameters
+            # unless you clearly know what this will cause.
+            "cfg_scale": 1,
+            "rand_device": self.pipe.device,
+            "use_gradient_checkpointing": self.use_gradient_checkpointing,
+            "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
+        }
+        inputs_shared = self.parse_extra_inputs(data, self.extra_inputs, inputs_shared)
+        return inputs_shared, inputs_posi, inputs_nega
+    
+    def forward(self, data, inputs=None):
+        if inputs is None: inputs = self.get_pipeline_inputs(data)
+        inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype)
+        for unit in self.pipe.units:
+            inputs = self.pipe.unit_runner(unit, self.pipe, *inputs)
+        loss = self.task_to_loss[self.task](self.pipe, *inputs)
+        return loss
+
+
+def anima_parser():
+    parser = argparse.ArgumentParser(description="Training script for Anima models.")
+    parser = add_general_config(parser)
+    parser = add_image_size_config(parser)
+    parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.")
+    parser.add_argument("--tokenizer_t5xxl_path", type=str, default=None, help="Path to tokenizer_t5xxl.")
+    return parser
+
+
+if __name__ == "__main__":
+    parser = anima_parser()
+    args = parser.parse_args()
+    accelerator = accelerate.Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        kwargs_handlers=[accelerate.DistributedDataParallelKwargs(find_unused_parameters=args.find_unused_parameters)],
+    )
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=args.dataset_repeat,
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_image_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+        )
+    )
+    model = AnimaTrainingModule(
+        model_paths=args.model_paths,
+        model_id_with_origin_paths=args.model_id_with_origin_paths,
+        tokenizer_path=args.tokenizer_path,
+        tokenizer_t5xxl_path=args.tokenizer_t5xxl_path,
+        trainable_models=args.trainable_models,
+        lora_base_model=args.lora_base_model,
+        lora_target_modules=args.lora_target_modules,
+        lora_rank=args.lora_rank,
+        lora_checkpoint=args.lora_checkpoint,
+        preset_lora_path=args.preset_lora_path,
+        preset_lora_model=args.preset_lora_model,
+        use_gradient_checkpointing=args.use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
+        extra_inputs=args.extra_inputs,
+        fp8_models=args.fp8_models,
+        offload_models=args.offload_models,
+        task=args.task,
+        device=accelerator.device,
+    )
+    model_logger = ModelLogger(
+        args.output_path,
+        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
+    )
+    launcher_map = {
+        "sft:data_process": launch_data_process_task,
+        "direct_distill:data_process": launch_data_process_task,
+        "sft": launch_training_task,
+        "sft:train": launch_training_task,
+        "direct_distill": launch_training_task,
+        "direct_distill:train": launch_training_task,
+    }
+    launcher_map[args.task](accelerator, dataset, model, model_logger, args=args)
--- a/examples/anima/model_training/validate_full/anima-preview.py
+++ b/examples/anima/model_training/validate_full/anima-preview.py
@@ -0,0 +1,21 @@
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+from diffsynth.core import load_state_dict
+import torch
+
+
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors"),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors"),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/")
+)
+state_dict = load_state_dict("./models/train/anima-preview_full/epoch-1.safetensors", torch_dtype=torch.bfloat16)
+pipe.dit.load_state_dict(state_dict)
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0)
+image.save("image.jpg")
--- a/examples/anima/model_training/validate_lora/anima-preview.py
+++ b/examples/anima/model_training/validate_lora/anima-preview.py
@@ -0,0 +1,19 @@
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors"),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors"),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/")
+)
+pipe.load_lora(pipe.dit, "./models/train/anima-preview_lora/epoch-4.safetensors")
+prompt = "a dog"
+image = pipe(prompt=prompt, seed=0)
+image.save("image.jpg")